Faecal metagenomics for GI pathogen detection, Brisbane
330 faecal samples from patients with gastrointestinal infection or diarrhoea, collected in Brisbane, Australia, in a cross-sectional study comparing metagenomic sequencing with PCR and culture-based pathogen detection. machine-written summary
330 samples · 330 runs · AU · Age unknown · 2025 · 4.71 Gbp / sample
Checked this project's papers and archive records yourself? Mark it human-verified.
what each source gave
| ✓ Sequence archive | 1,980 sample values (collection date, country, latitude, location locality, location region, longitude) |
| ✓ Abstract / description | 660 sample values (health condition, health condition detail) |
| ○ Full text | read; no usable statement or table · PMC11742996, PMC12231603 |
| ○ Supplementary tables | files read; no table could be matched to the samples |
| – External resources | not covered by the external resources we ingest |
| – User contribution | nothing uploaded yet · share metadata |
| – Human verified | not yet checked by a person · I checked this project — verify it |
Genera, mean relative abundance over 295 profiled gut samples of this project (Sandpiper SingleM profiles, GTDB R232; low-depth and non-metagenome profiles left out).
| field | samples | coverage | R1 | R2 | R3 | R4 |
|---|---|---|---|---|---|---|
| age_category | 0 | 0% | ||||
| country | 330 | 100% | 330 | |||
| health_condition | 330 | 100% | 330 | |||
| subject_id | 0 | 0% | ||||
| collection_date | 330 | 100% | 330 | |||
| detailed_location | 330 | 100% | ||||
| health_condition_detail | 330 | 100% | 330 | |||
| latitude | 330 | 100% | 330 | |||
| location_locality | 330 | 100% | 330 | |||
| location_region | 330 | 100% | 330 | |||
| longitude | 330 | 100% | 330 |
- Metagenomics: a new frontier for routine pathology testing of gastrointestinal pathogens. 2025 PMID 39827146 PMC11742996 own_data
- Advancing metagenomic classification with NABAS+: a novel alignment-based approach. 2025 PMID 40630933 PMC12231603 own_data
Archive description
BACKGROUND. Accurate and comprehensive identification of enteropathogens, causing infectious gastroenteritis, is essential for optimal patient treatment and effective isolation processes in health care systems. Traditional diagnostic techniques are well established and optimised in low-cost formats. However, thorough testing for a wider range of causal agents is time consuming and remains limited to a subset of pathogenic organisms. Metagenomic next-generation sequencing (mNGS) allows the identification of all pathogens in a sample in a single test, without a reliance on culture or introduction of target selection bias. This study aims to determine the ability to routinely apply mNGS testing, in comparison to traditional culture or polymerase chain reaction (PCR) based tests, for the identification of causal pathogens for gastrointestinal infections. RESULTS. The performance of mNGS, PCR and microscopy, culture and sensitivity (MCS) assays was established using 2,619 prospectively collected faecal samples from patients with symptomology indicative of infectious gastroenteritiss. Commonly experienced pathogens including Aeromonas spp, Campylobacter spp, Salmonella spp and Giardia spp, in single and co-infected patients, were used to establish test outcomes. When testing for these organisms, using the combined result from either or both PCR and MCS testing as the comparator, the mNGS assay had clinically acceptable sensitivity (89.2 - 100%). Further, the mNGS assay detected 14 additional enteropathogens, that were either not detected or not tested, by initial PCR/MCS testing. CONCLUSIONS. The advantage of mNGS compared to other syndromic testing systems is the broad range of detectable targets and the ability to interrogate samples without clinician informed or assay specific bias. With the development of newer sequencing assays, it is now feasible to test for a wide range of target organisms in a sample using a single mNGS test. Overall, the mNGS based approach enabled pathogen detection that was comparable to conventional diagnostics and was shown to have the potential to be extended for the detection of many pathogens and genes of clinical interest. In conclusion, the mNGS assay offers an easy, sample to answer workflow with rapid detection of enteropathogens and has the potential to improve diagnosis, therapy and infection control precautions.
How this project entered the registry
Classified as human gut / stool shotgun DNA metagenome of age unknown by sonnet_x2 with confidence 0.70. Assay shotgun_dna.
- host run.host_tax_id · “9606”
- body site study.description · “faecal samples from patients”
Evidence examples (one per field × route)
| sample | field | value | route | conf. | source | quote |
|---|---|---|---|---|---|---|
| SAMN43495243 | collection_date | 2019-07/2020-07 | R1 | 0.90 | biosample.attribute:collection_date | 2019-07/2020-07 |
| SAMN43495243 | country | AU | R1 | 0.90 | sample.attr.geo_loc_name | Australia: Brisbane |
| SAMN43495243 | health_condition | gi_infection_or_diarrhoea | R4 | 0.50 | study.description | patients with symptomology indicative of infectious gastroenteritiss |
| SAMN43495243 | health_condition_detail | infectious gastroenteritis with symptomology | R4 | 0.50 | study.description | patients with symptomology indicative of infectious gastroenteritiss |
| SAMN43495243 | latitude | -27.4705 | R1 | 0.90 | biosample.attribute:lat_lon | 27.4705 S 153.0260 E |
| SAMN43495243 | location_locality | Brisbane | R1 | 0.85 | biosample.attribute:geo_loc_name | Australia: Brisbane |
| SAMN43495243 | location_region | Queensland | R1 | 0.60 | biosample.attribute:lat_lon | 27.4705 S 153.0260 E |
| SAMN43495243 | longitude | 153.0260 | R1 | 0.90 | biosample.attribute:lat_lon | 27.4705 S 153.0260 E |
Sequencing
330 runs over 330 samples · mean 4.25 Gbp per run (median 4.71 Gbp, total 1,404.03 Gbp; 330 runs with a base count) · layout PAIRED 330 · platform ILLUMINA 330 · models Illumina NovaSeq 6000 (330) · 89% of runs with a Sandpiper profile · first public 2025-02-07 – 2025-02-23
Authors (18)
Angel NZ first · Sullivan MJ · Alsheikh-Hussain A · Fang L · MacDonald S · Pribyl A · Wills B · Tyson GW · Hugenholtz P · Parks DH · Griffin P · Wood DLA last · Takács B first · Jaksa G · Qorri E · Gyuris Z · Pintér L · Haracska L last
Help complete this project
2 of the 4 core fields are below 50% coverage: age_category, subject_id. Share metadata
Downloads
PRJNA1156595.csv.gz (7.1 KB) · parquet (80.6 KB) — all 330 samples, every field with route and confidence · PRJNA1156595_determinations.csv.gz (14.3 KB) — 2,640 evidence rows